{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using Seismic, PyPlot # required deps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "####################################################################\n",
    "### FLAT LAYER MODEL ###############################################\n",
    "####################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's first record some seismic data:\n",
    "\n",
    "include(\"../vel/flat.vel_correct.jl\")\n",
    "include(\"../mod/modeler.jl\");\n",
    "modeler(\"../dat/vel_correct\",\"../dat/image_correct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIOCAYAAADHmRXwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAH1dJREFUeJzt3XuM1/Wd7/HXb4ZBEGG4OiOIgrqKvaFQubRuWyt2sBfNRtaIurs21pigEsRsN7snaszpZtek0RCrdv3DvdmCm7bbmrVbejpZhZ6DqIC0e0QrVddaOqOg3AYFhN/5A85sJ174jgx85zs8Hskv0d/vM+N7PsPP75Pv71ar1+v1AADHtIayBwAAyicIAABBAAAIAgAgggAAiCAAACIIAIAIAgAgggAAiCAAAFJyENx7772ZPHlyhg4dmlmzZuWpp54qcxwAOGaVFgQPP/xwbrnlltxxxx1Zt25dpk6dmra2tmzevLmskQDgmFUr68ONZs2alZkzZ2bJkiVJknq9nokTJ2bhwoX5+te/XsZIAHDMKuUMwd69e7NmzZpceOGF3dfVarXMmTMnq1atKmMkADimDSrjP7p58+bs27cvLS0tPa5vaWnJ888//57rly9fnkmTJmXo0KFHa0wAqLy33norL7/8ctra2jJ27Nj3XVdKEPTW8uXLc/XVV5c9BgBU1kMPPZSrrrrqfW8vJQjGjh2bxsbGdHZ29ri+s7Mzra2t71o/adKkJMk///NDmTJlytEYkd+zePHNueuuu8se45hk78tj78tj7/vWc889lz/5k6u7j6Xvp5QgaGpqyvTp09Pe3p5LLrkkyYEnFba3t2fhwoXvWv//HyaYMmVKpk2bdlRnJWluHvkB+15LUsrzUo8JH7z3HEkDZ++rdx8dOHvfvxzqIffSHjJYvHhxrrnmmkyfPj0zZszI3XffnV27duWaa675gK+qHbxw9H3QvvudHFn2tzwDZe+r+HNUceb+qthelhYEl19+eTZv3pzbbrstnZ2dOeecc7J8+fKMGzeurJEA4JhV6pMKFyxYkAULFpQ5AgAQn2VAAVdcMb/sEY5Z9r489r489r4clXjZ4X+rp2pPjhkI5s+/Iva9HPa+PPa+PPa+rxXbS2cIAIBqnSGopZ6aagSAwooeNysWBF6IAgC9UfS46SEDAEAQAACCAACIIAAAIggAgAgCACAVe9mh9ykEgN4petysWBAceGsiAKCYosdNDxkAAIIAAKjYQwY+ywAAesdnGQAAPssAAChOEAAAggAAEAQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAASQaVPUBv1A9eAIBiih43KxYEtdRTK3sMAKiMosdNDxlwmAQa9G/uoxQjCDhMHsSB/s19lGIq9ZBB7eCDBgBAMUWPm84QAADVOkPgSYUA0DueVAgAFFapMwSeQwAAveM5BABAYRU7Q+AVtQDQG0WPm84QAACCAAAQBABABAEAEEEAAEQQAAARBABABAEAEEEAAEQQAAARBABAKvZZBvWDFwCgmKLHzYoFwYEPQAYAiil63PSQAQBQrTMEtYPnCACAYooeNysWBMU/1xkAKH7c9JABACAIAABBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAADkCATBHXfckYaGhh6Xj3zkI9237969OzfccEPGjh2b4cOHZ968eXnttdf6egwAoBeOyBmCj33sY+ns7ExHR0c6Ojry85//vPu2RYsW5dFHH833v//9rFixIps2bcpll112JMYAAAoadES+6aBBGTdu3Luu3759ex588MEsW7Ysn/3sZ5Mkf//3f5+zzz47Tz75ZGbMmHEkxgEADuGInCF44YUXMmHChJx++um5+uqr85vf/CZJsmbNmrzzzju58MILu9eeddZZOeWUU7Jq1aojMQoAUECfB8GsWbPyD//wD1m+fHm+/e1v56WXXspnPvOZdHV1paOjI4MHD86IESN6fE1LS0s6Ojr6ehQAoKA+f8igra2t+58/9rGPZcaMGTn11FPzL//yLxkyZMhhfe+bFy9Oc3Nzj+vmX3FF5s+ff1jfFwAGgqVLl2bpsmU9rtu2bVuhrz0izyH4fc3NzTnzzDOzcePGzJkzJ3v27Mn27dt7nCXo7OxMa2vrIb/X3XfdlWnTph3JcQGgsubPn/+uvySvXbs2088775Bfe8Tfh2Dnzp359a9/nfHjx2f69OkZNGhQ2tvbu29//vnn88orr2T27NlHehQA4H30+RmCP//zP89XvvKVnHrqqfntb3+b22+/PYMGDcoVV1yRESNG5Nprr83ixYszatSoDB8+PAsXLsynP/1przAAgBL1eRC8+uqrufLKK7Nly5aMGzcu559/fp544omMGTMmSXL33XensbEx8+bNy+7duzN37tzce++9fT0GANALfR4ES5cu/cDbjzvuuNxzzz255557+vo/DQB8SD7LAAAQBACAIAAAchTeh6Av1Q9eAIBiih43KxYEtdRTK3sMAKiMosfNSgVBUjt4AQCKKXbc9BwCAEAQAACCAACIIAAAUrEnFdbrBy4AQDFFj5uVC4L9+8uegt9Xq4k06M/cRxmwQeAPdv/i9wH9m/soAzII9u5N9uwpewoAqI69e4utq1QQ7NqV7NxZ9hQAUB27dhVbV6kgcIYAAHpnQJ4h6OxMRo4sewoAqI7OzmLrKhUE27YlW7aUPQUAVMe2bcXWVSoIXngh6eoqewoAqI5Nm4qtq1QQPPFEcsIJZU8BANVR9Mn4lQqCHTu68s47O8oeAwAq4623ip1ar1QQbNr0f9PY6GUGAFDUvn0vFFpXqSDYtWtzkuFljwEAFbK50KpKBUHyUpK3yh4CACqko9CqigXBC0leL3sIAKiQrYVWVSwIxiQZW/YQAFAhjYVWVSwIzk0yqewhAKBCXk7yw0OuqlQQnDBoaBprw8oeAwAqY199aHa+c+h1lQqCm8/+VU49wfsQAEBR/7Xzd/mfvzz0ukoFwaUnb8q0MbvLHgMAKmPtli0DLwg2bvptmrYX/JQGACAbC753caWC4H8/+2xebmgoewwAqIzf7t9faF2lgmD33r3elggAeqHoA+3+ug0ACAIAQBAAABEEAEAq9qTClpEjM7GpqewxAKAy9u/dm2w99AccVSoIPvPxj+cTI0eWPQYAVMYvtm5NVq485LpKBcHgoSMzZJhPOwSAogYXfN1hpYJgyYY/yNghE8seAwAqY/Pbvym0rlJB8IPfTEgyuewxAKBCBuA7FSa/TVIrewgAqJDfFlpVsSBYn+SVsocAgAp5s9CqigVBQ7x1AgD0RrHjZsWC4KNJTi57CACokFeT/K9DrqpYEIxLMr7sIQCgQoq97rBSQTBmzKQ0NU0pewwAqIy9e+vZsuXQ6yoVBCee+Ac5/vhPlD0GAFTGrl3vDMQgaMzIkY1ljwEAlbF1a2M2bDj0ukoFwfnnJ6eeWvYUAFAd//VfyeOPH3pdpYJg7NjkpJPKngIAqqOrq9i6SgVBS0tyslcdAkBhO3YUW1epIBg1Khk3ruwpAKA6OjqKratUEDQ1JccdV/YUAFAdTU3F1lUqCI4/PjnhhLKnAIDqOP74YusqFQQNDQcuAEAxRY+blQqCWk0QAEBv1GrF1lUqCBoa6mloqJc9Bj3UkvidQP/lPnqsK3rc9PdtDpP/0UD/5j5KMZU6Q1BLPTV/uAGgsKLHzYoFwYELAFBM0eOmhwwAAEEAAAgCACCCAACIIAAAIggAgAgCACCCAACIIAAAIggAgAgCACCCAACIIAAAIggAgAgCACCCAACIIAAAIggAgAgCACCCAABIMqjsAXqjfvACABRT9LjpDAEAULUzBLXUUyt7DACojKLHTWcIAIBqnSGoHTxHAAAUU/S4WbEgiAcMAKAXih43PWQAAAgCAEAQAAARBABABAEAEEEAAEQQAAARBABABAEAEEEAAEQQAAARBABABAEAEEEAAEQQAAARBABABAEAEEEAAEQQAAARBABABAEAkA8RBCtXrswll1ySCRMmpKGhIY888si71tx2220ZP358jj/++Fx00UXZuHFjj9vffPPNXHXVVWlubs6oUaPyta99LV1dXR/+pwAADkuvg6CrqyvnnHNO7rvvvtRqtXfdfuedd+Zb3/pWHnjggTz55JMZNmxY2trasmfPnu41V155ZTZs2JD29vY8+uijWbFiRa6//vrD+0kAgA9tUG+/YO7cuZk7d26SpF6vv+v2JUuW5NZbb82Xv/zlJMk//dM/paWlJT/84Q9z+eWXZ8OGDVm+fHnWrFmTc889N0lyzz335Etf+lK++c1vprW19XB+HgDgQ+jT5xC89NJL6ejoyIUXXth93YgRIzJz5sysWrUqSfLEE09k1KhR3TGQJHPmzEmtVsvq1av7chwAoKA+DYKOjo7UarW0tLT0uL6lpSUdHR3da0488cQetzc2Nmb06NHdawCAo6vXDxmUqX7wAgAUU/S42adB0Nramnq9ns7Ozh5nCTo7O7sfImhtbc1rr73W4+v27duXN95445DPH7h58eI0N4/scd0VV1yR+fPn99FPAADVtXTp0ixbtqzHddu2bS30tX0aBJMnT05ra2va29vziU98Ikmyffv2rF69OjfccEOSZPbs2dm6dWvWrVvXHQnt7e2p1+uZOXPmB37/u+66O9OmTevLkQFgwJg/f/67/pK8du3anHfeJw/5tb0Ogq6urmzcuLH7FQYvvvhi1q9fn9GjR2fixIlZtGhRvvGNb+SMM87IpEmTcuutt+bkk0/OpZdemiSZMmVK2tract111+X+++/Pnj17ctNNN2X+/PmHPENQSz01DxoAQGFFj5u9DoKnn346F1xwQWq1Wmq1Wm655ZYkyZ/92Z/lwQcfzNe//vXs2rUr119/fbZu3Zo//MM/zL//+79n8ODB3d/ju9/9bm688cbMmTMnDQ0NmTdvXpYsWdLbUegHavG8DujP3EcpqlZ/rzcT6GfWrl2b6dOnZ81TT3nIAAB6Ye3atZl+3nlZs2bNBx5DfZYBACAIAABBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAASQaVPUBv1A9eAIBiih43KxYEtdRTK3sMAKiMosdNDxlwmAQa9G/uoxRTqTMEB/5g+8Pd//idQP/mPnpsc4YAAChIEAAAggAAEAQAQAQBABBBAACkYi873L8/2bev7CkAoDr27y+2rlJBsGdPsnt32VMAQHXs2VNsXaWCYN8+ZwgAoDeKHjcrFQQ7dybbtpU9BQBUx86dxdZVKgjeeqv4DwYAHDh2FlGpINi2LXnjjbKnAIDqKHpmvVJB8OqryaBKTQwA5Xr11WLrKnV43bEj2bq17CkAoDp27Ci2rlJB8OyzSWdn2VMAQHVs2VJsXaWC4He/q2fHjnrZYwBAZezcWey4Wakg2Lx5f3buLPiWSwBA3n672HGzUkGwaVM9tZogAICi6vUBeIZg8OCksbHsKQCgOvbtS95++9DrKhUEp53WkGHDFAEAFNXV1ZBnnz30ukoFwcjmeppHeFIhABTVNGgAPmTwpbNfyuSThpY9BgBUxku/eyn/Z9Wh11UqCC4665VMO61SIwNAqdaOeCX/o8C6Sh1d39z4q7zeVfAtlwCAvPm73xVaV6kg2PC972VvU1PZYwBAZWzcu7fQukoFwc4330zBD20CAJLsLLiuUkGwJYmnFAJAcQU/yqBaQdCVZHvZQwBAhXQVXFepIDghyciyhwCACin6VPxKBcGYJC1lDwEAFbKr4LpKBcHwsWMzcvDgsscAgMoYvmdPsnnzIddVKgg+8sd/nE+MH1/2GABQGYM3bUruv/+Q6yoVBMNPPS2jTju97DEAoDKGNw0ptK5SQfDjDZPz7Jtnlz0GAFTGyx37C62rVBD89PlTM+J3Z5Y9BgBUxvbtxd6aqFJBsHVbsqfYOzACAEl2FXyZQaWC4MUX96ehYV/ZYwBAZezfPwAfMnjnnaRWK3sKAKiOer3YukoFwYQJDRkypLHsMQCgMt5+uyGvvHLodZUKgtGjaznhBKcI+pNarZZ60fwEjjr3UXburA28IDj55FrGjBEE/Y/fCfRv7qPHsi1bannmmUOvq1QQnH12MnFi2VMAQHX85jfJv/3boddVKghOOCFpbi57CgCojjffLLauUkEwcWJyxhllTwEA1bGv4Kv1KxUEw4cnI0eWPQUAVMfw4cXWVSoIjj+++A8GABw4dhZRqSAYNiwZMaLsKQCgOoYNK7auUkHQ2HjgAgAUU/S4WakgGDw4GVLsY50BgBw4dhZRqSBoaDhwAQCKKXrcdHgFAAQBAFCxhwyS+sELAFBMseNmpYKglnpqggAACit63KxYEPjMLgDojaLHTc8hAAAEAQAgCACACAIAIIIAAIggAAAiCACAfIggWLlyZS655JJMmDAhDQ0NeeSRR3rc/tWvfjUNDQ09Ll/84hd7rHnzzTdz1VVXpbm5OaNGjcrXvva1dHV1Hd5PAgB8aL0Ogq6urpxzzjm57777Uqu999sdXHzxxens7ExHR0c6OjqydOnSHrdfeeWV2bBhQ9rb2/Poo49mxYoVuf766z/cTwAAHLZev1Ph3LlzM3fu3CRJvf7eb4d43HHHZdy4ce9523PPPZfly5dnzZo1Offcc5Mk99xzT770pS/lm9/8ZlpbW3s7EgBwmI7Icwgee+yxtLS0ZMqUKVmwYEHeeOON7ttWrVqVUaNGdcdAksyZMye1Wi2rV68+EuMAAIfQ559lcPHFF+eyyy7L5MmT8+tf/zp/+Zd/mS9+8YtZtWpVarVaOjo6cuKJJ/b4msbGxowePTodHR19PQ4AUECfB8Hll1/e/c8f/ehH8/GPfzynn356HnvssVxwwQWH9b1vXrw4zc3NPa6bf8UVmT9//mF9XwAYCJYuXZqly5b1uG7btm2FvvaIf9rh5MmTM3bs2GzcuDEXXHBBWltb89prr/VYs2/fvrzxxhuHfP7A3XfdlWnTph3JcQGgsubPn/+uvySvXbs2088775Bfe8Tfh+DVV1/Nli1bctJJJyVJZs+ena1bt2bdunXda9rb21Ov1zNz5swjPQ4A8B56fYagq6srGzdu7H6FwYsvvpj169dn9OjRGT16dO64445cdtllaW1tzcaNG/MXf/EXOfPMM9PW1pYkmTJlStra2nLdddfl/vvvz549e3LTTTdl/vz5XmEAACXp9RmCp59+Oueee26mT5+eWq2WW265JdOmTcvtt9+exsbG/OIXv8ill16as846K9ddd13OO++8rFixIk1NTd3f47vf/W6mTJmSOXPm5Mtf/nI+85nP5O/+7u/69AcDAIrr9RmCz372s9m/f//73v6Tn/zkkN9j5MiReeihh3r7nwYAjhCfZQAACAIAQBAAABEEAEAEAQAQQQAARBAAABEEAEAEAQAQQQAARBAAABEEAEAEAQAQQQAARBAAABEEAEAEAQAQQQAARBAAAEkGlT1Ab9QPXgCAYooeN50hAACqdoaglnpqZY8BAJVR9LhZqSBIagcvAEAxxY6bHjIAAAQBACAIAIAIAgAglXtSoXciAIDeKXbcrFQQ1A6+8BAAKKbocbNiQeBFhwDQG0WPm55DAAAIAgBAEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBABBBAABEEAAAEQQAQAQBAJBkUNkD9Eb94AUAKKbocbNiQVBLPbWyxwCAyih63PSQAQBQrTMEtYPnCACAYooeN50hAACqdoYgnkEAAL1Q9LjpDAEAIAgAAEEAAEQQAAARBABABAEAEEEAAEQQAACp2BsT+bRDAOgdn3YIAPi0QwCgOEEAAFTrIQMffwwAvTMgP/645lLKZdnSpaXPcKxe7L29PxYv9r7vL0VUKggox9Jly8oe4Zhl78tj78tj78tRqYcMvOywPO+377UPuI2+YX/LMxD2vqr30SrO3F8V3UtnCDgs7rTQv7mPUlTFzhB4H4Iy2Pfy2Pvy2Pvy2Pu+VXQvKxEEb731VpLkueeeS/GnR9BXtm3blrVr15U9xjHJ3pfH3pfH3vetA8fO/z6Wvp9avV7v92eUvvOd7+Tqq68uewwAqKyHHnooV1111fveXokg2Lx5c5YvX55JkyZl6NChZY8DAJXx1ltv5eWXX05bW1vGjh37vusqEQQAwJHlVQYAgCAAAAQBABBBAABEEAAAqUAQ3HvvvZk8eXKGDh2aWbNm5amnnip7pAFp5cqVueSSSzJhwoQ0NDTkkUceedea2267LePHj8/xxx+fiy66KBs3bixh0oHlb/7mbzJjxoyMGDEiLS0t+aM/+qP86le/6rFm9+7dueGGGzJ27NgMHz488+bNy2uvvVbSxAPHt7/97UydOjXNzc1pbm7Opz71qfzkJz/pvt2+Hx1/+7d/m4aGhixevLj7Ontfjn4dBA8//HBuueWW3HHHHVm3bl2mTp2atra2bN68uezRBpyurq6cc845ue+++1KrvfvdIO+8885861vfygMPPJAnn3wyw4YNS1tbW/bs2VPCtAPHypUrc9NNN2X16tX52c9+lr179+YLX/hCj3cUW7RoUR599NF8//vfz4oVK7Jp06ZcdtllJU49MEycODF33nln1q5dmzVr1uTzn/98Lr300mzYsCGJfT8annrqqTzwwAOZOnVqj+vtfUnq/djMmTPrCxcu7P73/fv31ydMmFC/8847S5xq4KvVavUf/ehHPa476aST6nfddVf3v2/btq0+ZMiQ+sMPP3y0xxvQXn/99XqtVquvXLmyXq8f2OfBgwfXf/CDH3Svee655+q1Wq2+evXqssYcsEaPHl1/8MEH7ftRsGPHjvqZZ55Zb29vr3/uc5+r33zzzfV63Z/5MvXbMwR79+7NmjVrcuGFF3ZfV6vVMmfOnKxatarEyY49L730Ujo6Onr8LkaMGJGZM2f6XfSxrVu3plarZfTo0UmSNWvW5J133umx92eddVZOOeUUe9+H9u/fn2XLlmXXrl2ZPXu2fT8KbrjhhnzlK1/J5z//+R7XP/300/a+JP32w402b96cffv2paWlpcf1LS0tef7550ua6tjU0dGRWq32nr+Ljo6OkqYaeOr1ehYtWpTzzz8/H/nIR5Ic2PvBgwdnxIgRPdba+77xn//5n5k9e3befvvtDB8+PP/6r/+aKVOmZN26dfb9CFq2bFmeeeaZPP300++6rbOz096XpN8GARxrFixYkGeffTY///nPyx7lmDFlypSsX78+27Zty/e+97386Z/+aVasWFH2WAPaq6++mkWLFuVnP/tZmpqayh6H39NvHzIYO3ZsGhsb09nZ2eP6zs7OtLa2ljTVsam1tTX1et3v4gi68cYb8+Mf/ziPPfZYxo8f3319a2tr9uzZk+3bt/dYb+/7xqBBg3Laaafl3HPPzV//9V9n6tSpWbJkiX0/gtasWZPXX38906ZNS1NTU5qamvL4449nyZIlGTx4cFpaWrJ79257X4J+GwRNTU2ZPn162tvbu6+r1+tpb2/Ppz71qRInO/ZMnjw5ra2tPX4X27dvz+rVq/0u+sCNN96YH/3oR/mP//iPnHLKKT1umz59egYNGtRj759//vm88sormT179tEedcDbv39/du/ebd+PoDlz5uSXv/xlnnnmmaxfvz7r16/PJz/5yVx99dXd/9zU1GTvS9CvHzJYvHhxrrnmmkyfPj0zZszI3XffnV27duWaa64pe7QBp6urKxs3bkz94Idfvvjii1m/fn1Gjx6diRMnZtGiRfnGN76RM844I5MmTcqtt96ak08+OZdeemnJk1fbggULsnTp0jzyyCMZNmxY91mY5ubmDBkyJCNGjMi1116bxYsXZ9SoURk+fHgWLlyYT3/605kxY0bJ01fbX/3VX+Xiiy/OKaeckh07duQ73/lOHn/88fz0pz+170fQsGHDup8j8/vXjRkzJmeffXaS2PuylPsih0O7995766eeemp9yJAh9VmzZtWfeuqpskcakB577LF6rVarNzQ09Lh89atf7V5z++2310866aT60KFD61/4whfqL7zwQokTDwzvtecNDQ31f/zHf+xe8/bbb9dvvPHG+pgxY+onnHBCfd68efXOzs4Spx4Yrr322vrkyZPrQ4YMqbe0tNQvuuiient7e/ft9v3oueCCC7pfdliv2/uy1Or1g38lBACOWf32OQQAwNEjCAAAQQAACAIAIIIAAIggAAAiCACACAIAIIIAAIggAAAiCACAJP8PIeeQOxZMi0kAAAAASUVORK5CYII=",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f616a9abf90>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f616a807050>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's view an inline out of the \"real\" seismic data:\n",
    "\n",
    "seismic, seismic_h = SeisRead(\"../dat/image_correct\"); # load data\n",
    "SeisPlot(seismic[:,:,50], cmap = \"seismic\")            # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f616a814410>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f6169984c90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then let's assume we've picked a velocity model\n",
    "# and converted it to interval velocities.\n",
    "# We'll assume there are interpretive errors in\n",
    "# the model.\n",
    "\n",
    "include(\"../vel/flat.vel_incorrect.jl\")\n",
    "\n",
    "# An inline from our picked vel model looks like this:\n",
    "\n",
    "vel_initial, vel_h = SeisRead(\"../dat/vel_incorrect\");      # read data\n",
    "SeisPlot(vel_initial[:,:,50], cmap = \"viridis_r\")           # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f616a81d110>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f6169929510>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obviously, this model is incorrect or we'd see three \n",
    "# seismic events. Let's forward model this \n",
    "# incorrect vel model and view the output modeled seismic:\n",
    "\n",
    "modeler(\"../dat/vel_incorrect\",\"../dat/image_incorrect\")        # model seismic from our bad vels\n",
    "seis_incorrect, seismic_h = SeisRead(\"../dat/image_incorrect\"); # load data\n",
    "SeisPlot(seis_incorrect[:,:,50], cmap = \"seismic\")              # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3D Poststack full waveform inversion algorithm\n",
      "\n",
      "\n",
      "In this program FWI updates a velocity model one sample at a time,\n",
      "one trace at a time. The velocity sample is perturbed positively,\n",
      "negatively, and not at all. Then the algorithm models new seismic\n",
      "from all three of the perturbed velocity models, and compares them to\n",
      "the input (real) seismic data. Whichever perturbation generates the\n",
      "least amount of error is chosen to be correct, and input into the \n",
      "updated velocity model.\n",
      "This algorithm accepts six inputs in the following order:\n",
      "\n",
      "\t1. Initial velocity model\n",
      "\t2. Poststack 3D seismic volume in Seis format\n",
      "\t3. One dimensional wavelet in Seis format\n",
      "\t4. Output file name (updated vel model in Seis format)\n",
      "\t5. Velocity update increment percentage in decimal\n",
      "\t6. Maximum number of velocity update iterations\n",
      "\t7. Verbosity (see note)\n",
      "\n",
      "If verbose is 0 operation is silent. If verbose is 1 updates will\n",
      "print to stdout. If verbose is 2 debugging info will print to stdout.\n",
      "Here is an example to get you started on the syntax:\n",
      "\n",
      "\n",
      "fwi(\"../dat/vel_incorrect\", \"../dat/image_correct\", \"../dat/wav\", \"../dat/updated_vel\", .1, 5, 0)\n",
      "\n",
      "\n",
      "Please note this algorithm is written for clarity not speed, so\n",
      "use forgivingly small velocity models.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f61698fe450>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed time: 47.314072889 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f616a81d2d0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Therefore, if we tried to interpret on our bad velocity \n",
    "# model, we would never find our drilling target. Hence\n",
    "# we need to improve our velocity model somehow. So let's\n",
    "# use full waveform inversion.\n",
    "\n",
    "# Let's input our poorly picked velocity model and the \n",
    "# seismic data into an FWI algorithm and take a look\n",
    "# at the updated velocity model to see if there was any\n",
    "# improvement:\n",
    "\n",
    "# NOTE: RUNNING FWI MAY TAKE 5 MINUTES OR MORE...\n",
    "\n",
    "tic()                                               # start timer\n",
    "include(\"../fwi/fwi.jl\")                            # prep deps\n",
    "fwi(\"../dat/vel_incorrect\", \"../dat/image_correct\", \n",
    "\"../dat/wav\", \"../dat/updated_vel\", .03, 10, 0);     # FWI algorithm\n",
    "toc()                                               # end timer + print\n",
    "\n",
    "vel_updated, vel_h = SeisRead(\"../dat/updated_vel\") # load data\n",
    "SeisPlot(vel_updated[:,:,50], cmap = \"viridis_r\")   # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f6169826450>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f616978fa90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see here that the vel model has been improved considerably\n",
    "# by full waveform inversion. FWI has corrected the velocity \n",
    "# error in out model to the tune of about 3%. \n",
    "\n",
    "# For comparison, let's view the insitu velocities.\n",
    "# (This is the Earth model we used to \"record\" the \"real\"\n",
    "# seismic, above):\n",
    "\n",
    "\n",
    "vel_true, vel_h = SeisRead(\"../dat/vel_correct\"); # read data\n",
    "SeisPlot(vel_true[:,:,50], cmap = \"viridis_r\")        # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# As you can see, the FWI updated vel model is much closer\n",
    "# to the true velocity model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "####################################################################\n",
    "### PYRAMID DOME MODEL #############################################\n",
    "####################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's first record some seismic data:\n",
    "\n",
    "include(\"../vel/pyramid.vel_correct.jl\")\n",
    "include(\"../mod/modeler.jl\");\n",
    "modeler(\"../dat/vel_correct\",\"../dat/image_correct\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f616980f350>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f616869bb90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First let's view an inline out of the \"real\" seismic data:\n",
    "\n",
    "seismic, seismic_h = SeisRead(\"../dat/image_correct\"); # load data\n",
    "SeisPlot(seismic[:,:,50], cmap = \"seismic\")     # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6N5ZLZg/SGQOWESQRCPKXNvVICgZrjh0GAMyNSa5SvV3Rku2KTl6celiNQNAKzOJfnC4IAA1Tt0+ClPRanvfhtDyyEQAAYIZgXtXbAgMAyNf4x+YurSkgEMy39IRBb1kDD5ecMQAAaD5nkiJTEB9wmBQZsjWRQDDP0hMHg4pluwoAAPMoLTTMzoKP+yL4YhwMurWYi0DQLBP0H5CvPWCoS7/vACAX9R5zzeLfnHfJAYdVD9DZ/u/Of7QmEDSTJbMBkeIgUL2lkHICAGgZLkqaJDmLH56DpC9CIVlK6AIEgkaZoAdBulbl2FIIAC2pbl8ESw4vyt7u/L4IBIJGSvoPuOzEQdXODAAA2oNJQWTxMTHZiYbJrEGgjnyFRyBoFIunm+ITB8WSAAC0qWxiwMeP6+lzv4XJnx1aeEggmA2r7kKo5DunKgjQnhgA2lq9vghpz5kgMpl38Q1pXwSntl9CIBDMUrp9sHqJQBIzAwDQqdIZg4qUvvKz0MkXXDx70N55gEAwJ+khQ8wIAEBHq9sXwaT4dKOD7tGWumQzBQAAmAwzBNNBDwIAwFTafGsigWAq6Rc47TdgScVpspYEAOhezktBRTJfXWDYnlsTCQTT4RVvJ0y3FAIAIB20NVGB5AuS0kLDNkIgGK9eD4K0cJD+AwCARL2tiWmxuXmTc05WXXXe4n0RCAR1uGhsS6FUddYAKwQAgMlYfGJt6CVLX0UGTr4Qb1Fs5VeU3R0IJug/EHckjJcHWvhrBwBoIeN7Irjsr3EnxayuYPxzT4vMGHR3IJCSNJcUCGZLBOMOGwIAYJacl1RJZw2UFR9a6Fpq83/XBwKXNCQKKiYRAgAAjZa88FSUFB+6uC+Cd63VF6HrA0GGgkEAQIPVe05p1aNtWmiyAgAA5KV7ZgjGRzIb+9O1aFoDAHQuZ8lTk6/TECeHQsPuCQTSWAFhZMlWQhvXwhgAgOZzkSQzhRWTFO9AsCDfQsPODQQTbCmMtxOypRAAkA8njRUaKtmW6CRfjMNAfMDR/G9N7NxAII21J/ZjBwzFWwzzHRYAAONlh+H5JBSkPRECxQGhyaGgcwNB8uSf9iBgSQAA0LKqlrQlxVsTA5MVnfw8bU3s3EAgSbJs2oXlAQBAK5p0a2Ja4zYPT2JsOwQAAB0yQzDRKQ8sEwAAOkWTCw07IxBIk/ckIBgAANpM2gMh8IqXDFyyNTFoztbEjgkE9CQAAHSUeoWGoZMvWlN6ILRfIEinTKoPFErPF6ieHchrfAAAzNGEhYZmclHy3mRLvTmNzRjMYRmh/QKBFAeAiimo1C4RsDQAAOhk2dJ4OQkDgWQFF/+a4zJC+waC9Ohhz2wAAKDzZc91YxPl8SFGDTp+n22HAACgzQIBSwIAAEzMbOKt+FNoqyUDVzG5so31JCAgAAC6XLYLId1h55wssBm/5G+rQBCMmsIDpAAAACQ1tAfCjJcMnnjiCV144YU68sgjFQSBHnnkkYPuc+edd2rFihVatGiRPvnJT2r37t0173/rrbd0+eWX67DDDtOSJUt09dVXa3h4eMp/203wCwCAblP3+XB8D4QZmHEgGB4e1kknnaRvfOMbcnX2O95zzz36+te/rr/5m7/Rk08+qfe85z3auHGjSqVSdp/LLrtMzz//vAYGBvToo49q69atuvbaa2c6FAAA0CAzXjLYtGmTNm3aJEmyOoUL9957r+644w5dcMEFkqTvfOc76uvr0z//8z/r0ksv1fPPP68tW7Zo586dOvnkkyVJmzdv1vnnn6+vfOUr6u/vn8v/B+hssywW6jpN7hsPtJVpPmw0tIbglVde0eDgoM4555zstsWLF2v9+vXatm2bLr30Uv3kJz/RkiVLsjAgSRs2bJBzTtu3b9dFF13UyCEBncNM8iZXiRSUIinyLJkpeawLnKwYyhdDKWyvzVNAo43vgRCUp5cIGhoIBgcH5ZxTX19fze19fX0aHBzM7vO+972v5v1hGGrp0qXZfQDU57wpGI0U7B9VMFphK27CiqF8b1EWOBmBAN1uXKGhK+UQCADUkb6yj3x29vhsuYqXK0fxD7pRVCulp7WZXMUrGI1kPmn8MluBkwWBFCQV2iw/oI3U+26d7ndwQwNBf3+/zExDQ0M1swRDQ0PZEkF/f7/27t1b83FRFOnNN9+csn5g90v/R4VCb81t7+v7qPr6T2rQ/wBoApOCUqRgpBS/qo/m1o4z7t/B1ECNyCs4UFYwWokbvcyWc7KeUFFvj/zCAssPaDtDg89q79BzNbdVKiPT+tiGBoLVq1erv79fAwMD+uhHPypJ2rdvn7Zv367rr79eknTaaafp7bff1jPPPJOFhIGBAZmZ1q9fP+nnP+Z/XaBDFx/ZyCEDs1f9pGzJb/Wep83i9f701f0cZwlQK3v+9/E+q7nkgbhrnJOLvJw3mZtg75ZLfkv/MWYR0CL6+k866EXyb/b9Ujuf+vqUHzvjQDA8PKzdu3dnOwxefvll7dq1S0uXLtVRRx2lm266SV/4whd0zDHHaNWqVbrjjjv0/ve/PysWXLNmjTZu3KhrrrlG999/v0qlkj73uc/pM5/5DDsM0H68KShFcqWKXDmqexdXFQh4Zd/iTHKVSOH+eDbHggme6MNAvieU9RRkBWYR0BlmHAh27Nihs88+W845Oed06623SpL+4A/+QN/61rd02223af/+/br22mv19ttv67d+67f0ox/9SD09PdnneOCBB3TDDTdow4YNCoJAl1xyie69997G/a+AeeK8yZWrlgMmeb7nNWTrc5IUmRRVJv16WTGU1KOoEM7PwIB54KzeYQIt5umnn9batWu19uM3sGSA+VFVCOgqExcDusjXzBDwpN/5TMpmCPyCdIagzlfeSRYG8fvDgGUF5CZdMti5c6dOOeWUCe/HLgNgAq7iFYyUFI6UJ1wOmLR2AJ0r8goOeAUHKhNO/VgYyC8syPf2yC90BAK0PAIBuo8lT+BJm9CJuma6yMdbBZPtgjycQ6p6/p8yDHq5KJllKnspqH9Hcy4+RD4NDAQH5IRAgO5jkitXFByoxOv+dZcDLD7tyyehAJgpbwpG49mlMHD1z0ZwyaFKC4vyCwrx2QdATggE6CzTKYkxk4tMQakSP2BHzPej8dICRRdNsNykeJujN5MrhHJFi3c1TOd7mFkENAGBAJ0jXQIop6f51d/m55KtZa4czfnkQGBObKxWxVWiybc5FkNZIZRCwgCag0CAzuKTV/7JPnKe8NHqXCXZyTJSnvA+1hPK9fYoWuRkIVsd0RwEArSvdGugj/+MlwKqz/qnEBCtbTrfnyYlXS69glJFPp31Shs5BexgQGMQCNC+THHB1khZwYHyWCiwNCDkPUCgMVzFy/mS3Gg5XjFwTr6nIN9blF9QlEIRCjBnBAK0pvRVkGW/HSw7OCiKf1EciA5U3ash7YNhTnKBk4tCOe/j7ox1ixFd7TQEoQGTIBCg9aRLAeVIwWhFrhLV7y9jSnoEUByILmNSUPZyw0nPhQme6K0QyHoK8j0hnRsxJQIBWpOZgnKkYKSsYLTM9D9QxUmy5OAsNzrx/awYxqtnYSCjFhFTIBAgf2bZfu2sGDCyuD9AcigQE51Aral+JkzVzbfKsnKU9VdQGIwVJAIJAgHyZ4pnA/aXFBwoj50MaBQGAnMSxWccBAeSLY1BXIwYLeqRLSzGqYK6AiQIBJhfWbGgSV6Kjwi2pCgwmQKlHgCYs5qeC8nPnaVbc9PaGwtkaShwjoDQ5QgEmHeu4uVGk2ODK/Fpgs7HywQUBwJNlG7VfXdU4f5SXIwYJlsYFxZlxZD1uS5GIEDjTOcMdik+TbAcKThQiQ8Rau6oACSclGxfHOuvYGHceMkVQ1kxmN4yHbMIHYlAgIZyleSkwIqfuItgFBc6OU8XQSB3XgpKFUmSK1VUd4rAJVsY034K5IGORCBA4yRNg4KRkoKRZDkAQGszkytFCksTd2VU4OQXFBS9pyc+BIndCR2JQIDZSXcAeJ/1EnAmuVKUHLMazw7wsAG0tmn1U0jqfIKyl4KKrJIUIQZOFrqxokS0NQIBZs1FPj446EA5biWseN9zdtJgzuMD0CAWLyeEFa9geFRycWMlW1BQ1FuU9RRI/x2AQIDpqektkPwl3b5UjigOBDqYk+Kf/cjHpyRKct7Lh4FcZLJsG7FE74T2RSDA9CTrjEEpynoHVJ8fAKB7OEnm49NEQ+8VjATZKYj0TmhfBAJMj09OExwpKThQOWiLIa8DgC6TnGmQLhdKkvWEiiS5Ar0T2hGBAAdLawAq8ZJAdnBQ1luA+gCg241/DDApPmOkFEku6Z2gpOiQ3gltgUCAg5kUlKLsDPRs+2DVEagAMF78ImKsd4IFLik87JH1FsVcYmsjEHS7ur0FNFYwGHl2DACYUr3eCTIba9Nc8XHxoap6JtA7oaUQCFDVW6CSNDxJ2qZGE502CADTkMw2umhUwUgpfvIPA/meUH4BvRNaDYGg29Sb8rd4t0AwWo4PFpr/UQHoQDW9E8pJnUEYxNsWi6FMYf3HJGYNckEg6DbJ8cKunGwXNI2dJRAxGwCgySwpPFRJbrQydshR0iuBwsP8EAi6jU/OLd+fFP6kRxADQJPVnF9QqmTLBdYTFx76wMkC9ivmhUDQyazqGGFv2WxAkM0OxP0HAGC+1Lz+T1+PeB/PXJaCZAkh6ZMQxH+yhDA/CASdLDk4JBgpxwWD6RJB0oyImQEArcBVvEKfzFomTZN8T0G+tyi/oCCFBIL5QCDoFNWFOTZ2m4viA4aCUpSdQw4ArWKsT4LJJQ9e5pxc4OSiUM6bLJ3KpE9CUxEIOklkCsqVrAWxZEnBoJc8/QYAtAmzeHZzeKzwMJ41CGU9BVmBPgnNQCDoIM7H5wmE+0typejg9+cwJgCYDVfxY6ekSnGzpEXFuPAwDHhAawICQbuypP1wxScFg5YVDKaHCfHzAqAd1e2TYCZXTg5R85acdOji7Yqhy97G7BEI2pU3BaMVhfvLcqNluXT7INsIAXQibwpGywqy7YpOvhjKLyrKL+yRFQkDc0UgaAfV/QaSJ30XJUcLRz7+O02HAHSoen0STBYXHiZdWS1I7kmfhFkjELQJV47irYM12weTNTbCAIAu5CpewUg5PugoLTwshrIFRfmekO2KM0QgaDV1ew0ongUoVRSOlNk+CKDrxdsV490Iirsty5yTFpqiQiAZfRJmikDQirwlFbZV/QYqUXyWALMBADCB+LEzHCnHs6dBWngYyheDeKcCJkQgaEEu8goOlKu2D9YWCpJvAaCO5HRWV46yHklWCOUXFmXv6ZEFbFecDIEgbzW9BmysG2HFJ8cLG9+/ADAN9fokOO+zLdleyg45srRHAsWHGQJB3pLtg8GBuN9AFgq8z84TAADMUmQKDiSFh4GT5GTFQH5hMZ454NTDDIGgFfi4MMaVIrYPAkCDZK/701lYpauvoVScoOiwixGNAAAAgQAAALBkML/SAsLkdMH07aBUid/mzGEAaD5vCspeOlCRVbzkJAuSnghh0LVFhgSC+ZRsiQlHynGRS9KimB4EADB/4uOOSwpGSvE5BYGTX1CQ740LDbv1hEMCQbNk/QfG/u58Vf+BiufEQQCYZ2N9EdIXYSZ5Jxf6ZPbWxw/ZTrVbErtg1oBA0EyRVzAaKRgtZz0HnI/bFsv7qT8eANB8ZgrKFbl3494Ick4WOllPQX5BQVYM8x7hvCAQNEqd7SvOW3zk8IGKXKnCbAAAtCAnSZHJRZGkKJ7YLQTyklwhlBWkg9Z0O3DGgEDQKBYfOezKaf8ByxoSMRsAAO3FeYv7x6gUP46nswaFMD7MKFDHhQICQaNY0o1wf0nBgaoQQKEgALQfb3KjFYWlSvy2c/I9oXxvj3xvT3z0cYchEMxGWoziLT5iWEl74rJP6gNMjiAAAG1pfE8ESbJ01rfi5cqVuL1yUnRogcs6K7YzAsEsuXKU9R/Itg9WNykCAHQUV4kU7o97I6RBwHrCeLtiT6HttysSCGYj2UroKj4uFqwl8Sa7AAAQ4klEQVSwfRAAOpmT4sf+yMtF8W0WOPlAUtQZT6UcXQwAAAgEAACAJYOpJQWE2QmD6dvlSK5Cu2IA6FpJoWGQ7kQIxgoMLQzartCQQDANLvJxAeFIOa4ZyHoP0H8AALqWKd5uXo4UulJ8UyGQX1hUtKgo62mvp9j2Gu18yHoQVD3ZJ70HXCWigBAAIKmq0NDibrVpC4T4OcNkoa/thZA+ebTorAGBYDwzuVIUbycsR9k2wrg7FicOAgAm4X3SzTZSOBzErZULYdxNsSeM2yu3qO4OBPXW/y1ujRmMVuIvKiEAADAN6YyB85FUjvcmmnOyBQVZ4OQKgSzUwc89LTJj0N2BQIobWqRLAT7pQVCO5MqR5CkQAADMhUmVeNZA3uLDi1xcdGjFMC4+bI08QCCIT54qKRgpxUcPZ+dU5josAEAnMCUvOiMFIyVJ8S4Ev7Co6D09soVFtUoi6O5AYBorCElrBfIeEwCgYxzcF8GSGsTW26XWutUNAABg3nTPDEHdA4RMTq2X0gAAXSA7z6bOk1AOhYbdEwikuBthOVJQqsS7B5IdBdn2QgAAmi0pXg/3l+JTDp2ThU5WLMSFhoV8Ju+7KhA4bwpGKwqHR+VKlYNmBqgfAAA0XXr8fbI1UUpOOFzUoyhYEJ9VkMMTUucGArN4S2Hk45kBWTYbEBcQEgAAAPNv/HOPSXJVZ+CYj5+gLHCyIBgLCE1eRujcQODjhhPB/pKC0Xj/p6veUQAAQKuIvIKRsoLRssw5SU7WE8ov6pFfWJyXZYTODQRSHALSPgSEAABAC8pe95tJkeSSvggKneSrzsdpMrYdAgCADpohqLutEACADlH9PNeEeoLOCARpAWElipcIvGVbDF3FExYAAO0niosMZZLCQOaSPwtJD4SgsaGgMwKBxvUkqPjsiEgOHQIAtCNX8XJRScH+siTFZxUsLCpa1JweCO0XCNJX+2nvgeQ86Gx2IEpmB/IdJQAAszZWaJj9JkWSpYXy5UiKfHyoURB3UJzr1sT2CwRSPI1yoKLgQDleFki2EsZLBT7v0QEA0HgWH67nKl7hcPzEb4VAfkFRvrcoK4Zz+vRtGQicSS7y8RHEoxVmAwAAHc9JyYvf+IRDk6QokAsDyc/96ZxthwAAgEAAAADabMkgGC0rKJalqKonAQAAXWp8DwRzTgri7okKZtYkqa0CQfj2iAoH3s3WUThfAADQ1SKv4EA5DgTJLgPrCeV7Z94Doa0CgYu8ggq7CAAAyF78e5MUb7c3KT6waBYz6NQQAAAAAgEAAGizJQMAADCFyCsYLUtmsjBQsL80rQ8jEAAA0EFqeiA4qXBgZFofRyAAAKBDHNQDwTTtHXnUEAAAAAIBAAAgEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAAM0iEDzxxBO68MILdeSRRyoIAj3yyCM17//DP/xDBUFQ8+u8886ruc9bb72lyy+/XIcddpiWLFmiq6++WsPDw3P7nwAAgFmbcSAYHh7WSSedpG984xtyztW9z7nnnquhoSENDg5qcHBQDz74YM37L7vsMj3//PMaGBjQo48+qq1bt+raa6+d3f8AAADMWWGmH7Bp0yZt2rRJkmRmde+zYMECLV++vO77XnjhBW3ZskU7d+7UySefLEnavHmzzj//fH3lK19Rf3//TIcEAADmqCk1BI8//rj6+vq0Zs0aXXfddXrzzTez923btk1LlizJwoAkbdiwQc45bd++vRnDAQAAU5jxDMFUzj33XF188cVavXq1fv7zn+v222/Xeeedp23btsk5p8HBQb3vfe+r+ZgwDLV06VINDg42ejgAAGAaGh4ILr300uzvH/rQh/SRj3xEH/zgB/X444/r7LPPntPnfuHXj6kQLKi57YhD1mjFIcfP6fMCANAJfvXu8/p/775Qc1vFj07rYxseCMZbvXq1li1bpt27d+vss89Wf3+/9u7dW3OfKIr05ptvTlk/sObws3XYgr5mDhcAgLa14pDjD3qR/M7okLb98rtTfmzTzyF47bXX9Otf/1pHHHGEJOm0007T22+/rWeeeSa7z8DAgMxM69evb/ZwAABAHTOeIRgeHtbu3buzHQYvv/yydu3apaVLl2rp0qW6++67dfHFF6u/v1+7d+/W5z//eR177LHauHGjJGnNmjXauHGjrrnmGt1///0qlUr63Oc+p8985jPsMAAAICczniHYsWOHTj75ZK1du1bOOd1666065ZRTdNdddykMQz333HO66KKLdNxxx+maa67Rxz/+cW3dulXFYjH7HA888IDWrFmjDRs26IILLtBZZ52lv/7rv27ofwwAAEzfjGcIPvGJT8h7P+H7//Vf/3XKz/He975X3/3u1OsZAABgftDLAAAAEAgAAACBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAAAkFfIewIyYyczyHgUAAO1jms+bbRUI/L7fyBcW5D0MAADahq/8Zlr3a6tAYO8Oy1xP3sMAAKBtmA1P637UEAAAAAIBAAAgEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIMIVBezXvIXQlrns+uO754Lq3BgIBJjWoX+Q9hK7Edc8H1z0fXPfWQCAAAAAEAgAAQCAAAACSCnkPYDpGRkYkScPaJ1nOg+kyFZW1z97Kexhdh+ueD657PrjuzTWsfZLGnksn0haBYM+ePZKk/6un8h1Il3pSA3kPoStx3fPBdc8H17359uzZozPOOGPC9zsza/nX3G+88Ya2bNmiVatWqbe3N+/hAADQNkZGRrRnzx5t3LhRy5Ytm/B+bREIAABAc1FUCAAACAQAAIBAAAAARCAAAAAiEAAAALVBILjvvvu0evVq9fb26tRTT9VTT3EWQSN96Utf0rp167R48WL19fXp937v9/Tf//3fNfcZHR3V9ddfr2XLlunQQw/VJZdcor179+Y04s705S9/WUEQ6JZbbslu47o3x69+9StdeeWVWrZsmRYtWqQTTzxRTz/9dM197rzzTq1YsUKLFi3SJz/5Se3evTun0XYG773uuOMOfeADH9CiRYt0zDHH6Atf+MJB9+O658xa2EMPPWQLFiywb3/72/b888/bH/3RH9mSJUvs9ddfz3toHePcc8+173znO/azn/3MnnvuOTv//PNt5cqVtn///uw+f/zHf2wrV660xx9/3J5++mk77bTT7Mwzz8xx1J3lySeftNWrV9tJJ51kN998c3Y7173x3nrrLVu1apV99rOftR07dtiePXvs3/7t3+zll1/O7vPlL3/ZlixZYj/84Q/tpz/9qV100UX2gQ98wEZHR3MceXv74he/aMuXL7cf/ehH9j//8z/2ve99zw499FDbvHlzdh+ue/5aOhCsX7/ebrzxxuxt770deeSRds899+Q4qs72+uuvm3POnnjiCTMze+edd6ynp8e+//3vZ/d54YUXzDln27dvz2uYHeM3v/mNHXvssTYwMGC//du/nQUCrntzfP7zn7ezzjpr0vscccQR9tWvfjV7+5133rGFCxfaww8/3OzhdawLLrjArr766prbLr74Yrvyyiuzt7nu+WvZJYNyuaydO3fqnHPOyW5zzmnDhg3atm1bjiPrbG+//bacc1q6dKkkaefOnapUKjVfh+OOO05HH300X4cGuP766/WpT31Kv/M7v1Nz+44dO7juTfDDH/5QH/vYx3TppZeqr69Pp5xyiv72b/82e/8rr7yiwcHBmuu+ePFirV+/nus+B6effroGBgb00ksvSZJ27dql//zP/9R5550nieveKlq2l8Ebb7yhKIrU19dXc3tfX59efPHFnEbV2cxMN910k84880ydcMIJkqTBwUH19PRo8eLFNfft6+vT4OBgHsPsGA899JCeffZZ7dix46D3DQ0Ncd2b4OWXX9b999+vW2+9VX/xF3+hJ598UjfeeKMWLFigK6+8UoODg3LO1X3c4brP3p//+Z9r3759WrNmjcIwlPdeX/ziF/XpT39akrjuLaJlAwHm33XXXaef/exn+vGPf5z3UDrea6+9pptuukn//u//rmKxmPdwuob3XuvWrdNf/uVfSpJOPPFE/dd//Ze++c1v6sorr8x5dJ3r4Ycf1gMPPKCHHnpIJ5xwgp599ln96Z/+qVasWMF1byEtu2SwbNkyhWGooaGhmtuHhobU39+f06g61w033KB/+Zd/0eOPP64VK1Zkt/f396tUKmnfvn019+frMDc7d+7U66+/rlNOOUXFYlHFYlH/8R//oXvvvVc9PT3q6+vT6Ogo173BjjjiCB1//PE1tx1//PF69dVXJcXf72bG406D3Xbbbbr99tv1+7//+/rQhz6kyy+/XDfffLO+9KUvSeK6t4qWDQTFYlFr167VwMBYS0wz08DAgE4//fQcR9Z5brjhBv3gBz/QY489pqOPPrrmfWvXrlWhUKj5Orz44ot69dVXddppp833UDvGhg0b9NOf/lTPPvusdu3apV27duljH/uYrrjiiuzvxWKR695gZ5xxxkFLji+++KJWrlwpSVq9erX6+/trrvu+ffu0fft2HnfmYP/+/XLO1dwWBIG895K47i0j35rGyT388MPW29tbs+1w6dKltnfv3ryH1jH+5E/+xN773vfa1q1bbXBwMPs1MjJSc59Vq1bZY489Zjt27LDTTz+d7W9NUL3LwIzr3gxPPfWU9fT02F/91V/Z7t277R//8R/tkEMOsQcffDC7zz333GNLly61Rx55xJ577jm76KKL7JhjjmH72xxcddVVdtRRR9mjjz5qe/bsse9///u2fPlyu/3227P7cN3z19KBwMzsvvvus5UrV9rChQvt1FNPtaeeeirvIXUU55wFQXDQr29/+9vZfQ4cOGA33HCDHX744XbIIYfYJZdcYkNDQzmOujOdffbZNYGA694cjz76qH3kIx+x3t5eO+GEE+zv/u7vDrrPXXfdZUcccYT19vba7/7u79pLL72Uw0g7x7vvvms333yzrVq1yhYtWmTHHHOM3XnnnVYul2vux3XPlzMzy3uWAgAA5KtlawgAAMD8IRAAAAACAQAAIBAAAAARCAAAgAgEAABABAIAACACAQAAEIEAAACIQAAAAEQgAAAAkv4/J7hcz+2TbasAAAAASUVORK5CYII=",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f61686dd050>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f61685c8c90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then let's assume we've picked a velocity model\n",
    "# and converted it to interval velocities.\n",
    "# We'll assume there are interpretive errors in\n",
    "# the model.\n",
    "\n",
    "include(\"../vel/pyramid.vel_incorrect.jl\")\n",
    "\n",
    "# An inline from our picked vel model looks like this:\n",
    "\n",
    "vel_initial, vel_h = SeisRead(\"../dat/vel_incorrect\"); # read data\n",
    "SeisPlot(vel_initial[:,:,50], cmap = \"viridis_r\")      # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f6168609110>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f616970ad90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obviously, this model is incorrect. Hypothetically the \n",
    "# geophysicist who picked this section was afraid to pick\n",
    "# the velocity inversion. Let's forward model this \n",
    "# incorrect vel model and view the output modeled seismic:\n",
    "\n",
    "modeler(\"../dat/vel_incorrect\",\"../dat/image_incorrect\")        # model seismic from our bad vels\n",
    "seis_incorrect, seismic_h = SeisRead(\"../dat/image_incorrect\"); # load data\n",
    "SeisPlot(seis_incorrect[:,:,50], cmap = \"seismic\")              # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3D Poststack full waveform inversion algorithm\n",
      "\n",
      "\n",
      "In this program FWI updates a velocity model one sample at a time,\n",
      "one trace at a time. The velocity sample is perturbed positively,\n",
      "negatively, and not at all. Then the algorithm models new seismic\n",
      "from all three of the perturbed velocity models, and compares them to\n",
      "the input (real) seismic data. Whichever perturbation generates the\n",
      "least amount of error is chosen to be correct, and input into the \n",
      "updated velocity model.\n",
      "This algorithm accepts six inputs in the following order:\n",
      "\n",
      "\t1. Initial velocity model\n",
      "\t2. Poststack 3D seismic volume in Seis format\n",
      "\t3. One dimensional wavelet in Seis format\n",
      "\t4. Output file name (updated vel model in Seis format)\n",
      "\t5. Velocity update increment percentage in decimal\n",
      "\t6. Maximum number of velocity update iterations\n",
      "\t7. Verbosity (see note)\n",
      "\n",
      "If verbose is 0 operation is silent. If verbose is 1 updates will\n",
      "print to stdout. If verbose is 2 debugging info will print to stdout.\n",
      "Here is an example to get you started on the syntax:\n",
      "\n",
      "\n",
      "fwi(\"../dat/vel_incorrect\", \"../dat/image_correct\", \"../dat/wav\", \"../dat/updated_vel\", .1, 5, 0)\n",
      "\n",
      "\n",
      "Please note this algorithm is written for clarity not speed, so\n",
      "use forgivingly small velocity models.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f616967f110>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed time: 537.849204341 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f61695ddf90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Therefore, if we tried to interpret on our bad velocity \n",
    "# model, we would never find our drilling target. Hence\n",
    "# we need to improve our velocity model somehow. So let's\n",
    "# use full waveform inversion.\n",
    "\n",
    "# Let's input our poorly picked velocity model and the \n",
    "# seismic data into an FWI algorithm and take a look\n",
    "# at the updated velocity model to see if there was any\n",
    "# improvement:\n",
    "\n",
    "# NOTE: RUNNING FWI MAY TAKE 10 MINUTES OR MORE...\n",
    "\n",
    "tic()                                               # start timer\n",
    "fwi(\"../dat/vel_incorrect\", \"../dat/image_correct\", \n",
    "\"../dat/wav\", \"../dat/updated_vel\", .03, 10, 0);     # FWI algorithm\n",
    "toc()                                               # end timer + print\n",
    "\n",
    "vel_updated, vel_h = SeisRead(\"../dat/updated_vel\") # load data\n",
    "SeisPlot(vel_updated[:,:,50], cmap = \"viridis_r\")   # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f61685b2850>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "PyObject <matplotlib.image.AxesImage object at 0x7f6169633e90>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see here that the vel model has been improved considerably\n",
    "# by full waveform inversion. Though the edges of the reservoir\n",
    "# are smeared (because the wavelet envelope comparison algorithm\n",
    "# isn't sophisticated enough to handle tuning), you are clearly \n",
    "# able to delinate where the reservoir is. \n",
    "\n",
    "# For comparison, let's view the insitu velocities.\n",
    "# (This is the model the author used to create the \"real\" seismic)\n",
    "\n",
    "\n",
    "vel_true, vel_h = SeisRead(\"../dat/vel_correct\"); # read data\n",
    "SeisPlot(vel_true[:,:,50], cmap = \"viridis_r\")        # plot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# As you can see, the FWI updated vel model is much closer\n",
    "# to the true velocity model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
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